Direct neural interface systems, also known as brain-computer interfaces (BCI) allow using electrophysiological signals issued by the cerebral cortex of a human or animal subject for driving an external device. BCI have been the subject of intense research since the seventies. At present, a human subject or an animal can drive “by the thought” a simple device, such as a cursor on a computer screen. In 2006, a tetraplegic subject has even been able to drive a robotic arm through a BCI. See the paper by Leigh R. Hochberg et al. “Neuronal ensemble control of prosthetic devices by a human with tetraplegia”, Nature 442, 164-171 (13 Jul. 2006).
Until now, the best results in this field have been obtained using invasive systems based on intracortical electrodes. Non-invasive systems using electroencephalographic (EEG) signals have also been tested, but they suffer from the low frequency resolution of these signals. Use of electrocorticographic (ECoG) signals, acquired by intracranial electrodes not penetrating the brain cortex, constitutes a promising intermediate solution.
Conventional BCI systems use a limited number of “features” extracted from EEG or ECoG signals to generate command signals for an external device. These features can be related e.g. to the spectral amplitudes, in a few determined frequency bands, of ECoG signals generated by specific regions of the cortex when the subject imagine performing predetermined action. As a result, only a few features of the signal are used, while the other features of the signal are not taken into account.
For example, in the paper by Schalk G., Kubanek J., Miller K. J., Anderson N. R., Leuthardt E. C., Ojemann J. G., Limbrick D., Moran D. W., Gerhardt L. A. and Wolpaw J. R. “Decoding two-dimensional movement trajectories using electrocorticographic signals in humans” J. Neural. Eng. 4 (2007), 264-75, a subject has been trained to modulate the spectral amplitude of the signals issued by a few Brodmann regions of its cortex in several frequency bands.
This approach is not completely satisfactory as, for any different command signal to be generated (e.g. vertical or horizontal movement of a cursor on a screen) it is necessary to identify different features, associated to different actions imagined by the subject and substantially uncorrelated from each other. Especially if the number of different commands signals to be generated is greater than two or three, this can get very complicated. Moreover, this approach is intrinsically inefficient as only a small amount of the information carried by the acquired ECoG signals is exploited.
The paper by K. Nazarpour et al. “Parallel Space-Time-Frequency Decomposition of EEG Signals of Brain Computer Interfacing”, Proceedings of the 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, Sep. 4-8, 2006 discloses a method of processing EEG signals, based on multi-way analysis. In the method described by this paper, EEG signals are acquired by 15 electrodes disposed on a subject's scalp. The acquired signals are preprocessed, which includes spatial filtering, digitization and wavelet transform. Preprocessed data are arranged in a three-way tensor, the three ways corresponding to space (i.e. electrode location on the subject's scalp), time and frequency. A tensor corresponding to signals acquired over a 3-second observation window during which the subject imagines moving either the left of the right index is decomposed using the well-known PARAFAC (PARallel FACtors) technique. Then classification is performed using SVM Method (Support Vector Machine). As traditional classification methods, SVM enables the classification of observation vectors. This is why the tensor corresponding to signals is projected on one dimension, namely the spatial dimension, before the classification is carried out. The spatial signatures of the first two PARAFAC factors are fed to a suitable classifier which discriminates between a left index and right index imaginary movement. This method suffers from a few important drawbacks.
First of all, as only the spatial signatures of the PARAFAC factors are used, a large amount of the available information is lost. Furthermore PARAFAC is applied to decompose EEG signal tensor before and independently of classification. Being a generalization of principal component analysis (PCA), PARAFAC projects the tensor to a low dimensional space trying to explain the variability of observations (EEG), keeping the dominant (i.e. most informative) components of signal, but without taking into account their relevance for discrimination. Otherwise stated, non event-related information (useless for discrimination) can be retained, while event-related (and therefore useful for discrimination) components having low amplitude can be lost.
Moreover, a “human” intervention is still required to associate the classifier output to the left or to the right index movement. In other words, this step, the so-called calibration procedure, is not carried out automatically.
Also, only a rather narrow frequency band is considered (μ band). This band is known to be usable in EEG-based BCI. Otherwise stated, like in “classical” method there is a pre-selection of only a small portion of the available information.
Most prior art BCI systems—including the previously-described one by K. Nazarpour et al.—are based on a “cue-paced”, or synchronized, approach where subjects are waiting for an external cue that drives interaction. As a result users are supposed to generate commands only during specific periods. The signals outside the predefined time windows are ignored. However, in a real-life environment this restriction would be very burdensome. As opposed to the “cue-paced” systems, no stimulus is used by “self-paced” BCIs. However, the performances of prior-art self-paced BCIs are not suitable for practical application in particular because of a high level of false system activation, which causes frustration of users and limits the application of the system. Moreover, prior art self-paced BCI experiments were carried out in laboratory conditions, which differ significantly from natural environment where users are not concentrated properly, can be disturbed by external noises, etc. In the majority of prior art self-paced experiments, session time does not exceed several minutes, which is not enough to verify BCI performance. Finally, duration of experiment series is short enough to neglect long-term brain plasticity effects. For examples of prior art self-paced BCI, see e.g.:                C. S. L. Tsui, J. Q. Gan, S. J. Roberts “A self paced brain-computer interface for controlling a robot simulator: an online event labeling paradigm and an extended Kalman filter based algorithm for online training”, Med Biod Eng Comput (2009) 47:257-265        Fatourechi, R. K. Ward and G. E. Birch, “A self-paced brain-computer interface system with a low false positive rate,” J Neural Eng 5:9-23. doi:10.1088/1741-2560/5/1/002.        